Field of Invention
The present invention relates to a technical field of process monitoring of an industrial process, and more particularly to a fault diagnosis device based on common information and special information of running video information for an electric-arc furnace and a method thereof.
Description of Related Arts
Nowadays, process monitoring of multivariate statistical methods has become more and more mature. However, with the diversification and large scale of process variables, process monitoring becomes more and more complex. Especially after the video and audio data reflecting more and more abundant information, fault detection and diagnosis is facing enormous challenges.
Roughly speaking, different scholars from different angles, different classifications are made for the process monitoring method with the research deepening of fault detection and diagnosis methods. Professor Frank P. M. divides the process monitoring method into three categories: analytical model-based approach, qualitative knowledge-based approach and signal-based approach. However, with the deepening of research and the cross between disciplines deeper and deeper, such as the introduction of principal component analysis (PCA), independent component analysis (ICA) and other statistical theory, the multivariable statistical method based on data has been widely used in process monitoring and has shown a powerful advantage in the field of fault detection and diagnosis. Due to the data-based essence of multivariable statistical process monitoring, it is relatively easy to apply real processes of rather large scale, in comparison with other methods based on systems theory or rigorous process models. Therefore, fault monitoring methods based on data-driven will be listed as the fourth effective means by some scholars in the field of process monitoring.
The PCA method is used to extract useful information in multivariate process data in order to detect and identify various faults in the metallurgical and chemical industry. Wise BM et al. are the first to use PCA method in the field of process monitoring. Since then PCA as one of the basic methods of process monitoring makes multivariate statistical methods cut a figure in the application to fault detection and diagnosis. On the basis of PCA using in the field of fault detection and diagnosis, fault identification and fault reconstruction based on multivariate statistical methods have also flourished. However, in some cases, there are often dependencies between the two sets of multiple correlation variables in practical problems. This requires an effective method to model these two sets of variables reasonably. Thus, the partial least squares (PLS) method that could achieve the regression model of two sets of related variables came into being in 1983. After that, PLS is applied to process monitoring in order to deal well with the relationship between process variables and quality variables in the chemical industry. In addition, PCA and PLS methods are only suitable for the Gaussian process. So then, for non-Gaussian process, ICA plays an important role in extracting non-Gaussian variable information, which can make full use of the high-order statistics information from process data. Kano et al. applied ICA theory to fault diagnosis firstly, and meanwhile process monitoring method based on ICA was presented. Subsequently, in order to solve the serious nonlinear problems in the complex process industry, the kernel theory was used to form the KPCA, KPLS and KICA in the above multivariate statistical methods. They can demonstrate good performance in actual process monitoring.
Unfortunately, the above multivariate statistical method cannot show satisfactory fault detection results for video information of large data age compared to fault detection and diagnosis of traditional process variables. Multi-view video summarization is a good way to deal with large-scale video data, which opened the way to use video data for process monitoring. Video summarization technology is a summary of the original video content. By the analysis of the original video and key shots extraction, we can select meaningful video content to compose the compact video summarization. A good video summarization allows the user to obtain the maximum amount of information from the original video sequence in a minimum amount of time. Most of the traditional video summarization techniques are just for the single-view video. But with the development of video surveillance system, more and more video is multi-view video, and the scene contents are captured by different video cameras which often have similarity or dissimilarity, so the use of multi-view video summarization for fault detection and diagnosis has a great advantage. Furthermore, the multi-view video summarization technology provides a new idea for us to deal with multi-batch processes and long-running short-cycle reciprocating process problems.
In the research field which bases on the data of complex industrial process abnormal condition diagnosis and its applications, as shown in FIG. 1, three kinds of works are always exist, which are abnormal condition detection, abnormal condition isolation and abnormal condition identification respectively. The main objectives of these three works are whether there are faults, where the faults exist, estimating the kinds of faults (known faults or unknown faults), and then identify the known fault belongs to which kind of faults. Therefore, the abnormal condition detection works have plenty of research characteristics, such as complex evolution of fault caused by complex conditions, widely spreading of fault leaded by strong coupling of variables, and the weakening feature of fault as a result of multi-source interference. Because of these characteristics, three technical challenges have emerged, which are the difficulty of real-time detection, the difficulty of accurate isolation, and the difficulty of precise identification, separately. The background of this research is mainly based on the process of smelting and recrystallization of EFMF, which is a complex and variable strong coupling process. Besides, once the corresponding fault occurs in the period of industrial production, there will be a lot of unpredictable losses and risks, if a timely alarm or diagnosis cannot be given. Hence, in order to solve the problems as mentioned before, a data driven fault diagnosis approach based on multi-view will be applied to monitor the smelting process of EFMF by using the video information. And then we will introduce the fault diagnosis device based on multi-view method.